Overview

Dataset statistics

Number of variables18
Number of observations45466
Missing cells64196
Missing cells (%)7.8%
Duplicate rows16
Duplicate rows (%)< 0.1%
Total size in memory5.9 MiB
Average record size in memory137.0 B

Variable types

Boolean2
Numeric6
Text8
DateTime1
Categorical1

Alerts

Dataset has 16 (< 0.1%) duplicate rowsDuplicates
adult is highly imbalanced (99.7%)Imbalance
status is highly imbalanced (97.0%)Imbalance
video is highly imbalanced (97.9%)Imbalance
homepage has 37684 (82.9%) missing valuesMissing
overview has 954 (2.1%) missing valuesMissing
tagline has 25054 (55.1%) missing valuesMissing
popularity is highly skewed (γ1 = 29.22545384)Skewed
budget has 36573 (80.4%) zerosZeros
revenue has 38052 (83.7%) zerosZeros
runtime has 1558 (3.4%) zerosZeros
vote_average has 2998 (6.6%) zerosZeros
vote_count has 2899 (6.4%) zerosZeros

Reproduction

Analysis started2024-04-25 21:21:00.433713
Analysis finished2024-04-25 21:21:05.736157
Duration5.3 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

adult
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size355.3 KiB
False
45454 
True
 
9
(Missing)
 
3
ValueCountFrequency (%)
False 45454
> 99.9%
True 9
 
< 0.1%
(Missing) 3
 
< 0.1%
2024-04-25T17:21:05.755827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

budget
Real number (ℝ)

ZEROS 

Distinct1223
Distinct (%)2.7%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4224578.8
Minimum0
Maximum3.8 × 108
Zeros36573
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2024-04-25T17:21:05.797536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25000000
Maximum3.8 × 108
Range3.8 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17424133
Coefficient of variation (CV)4.1244662
Kurtosis66.765616
Mean4224578.8
Median Absolute Deviation (MAD)0
Skewness7.125326
Sum1.9206203 × 1011
Variance3.036004 × 1014
MonotonicityNot monotonic
2024-04-25T17:21:05.850551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36573
80.4%
5000000 286
 
0.6%
10000000 259
 
0.6%
20000000 243
 
0.5%
2000000 242
 
0.5%
15000000 226
 
0.5%
3000000 223
 
0.5%
25000000 206
 
0.5%
1000000 197
 
0.4%
30000000 190
 
0.4%
Other values (1213) 6818
 
15.0%
ValueCountFrequency (%)
0 36573
80.4%
1 25
 
0.1%
2 14
 
< 0.1%
3 9
 
< 0.1%
4 8
 
< 0.1%
5 8
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
380000000 1
 
< 0.1%
300000000 1
 
< 0.1%
280000000 1
 
< 0.1%
270000000 1
 
< 0.1%
260000000 3
 
< 0.1%
258000000 1
 
< 0.1%
255000000 1
 
< 0.1%
250000000 10
< 0.1%
245000000 2
 
< 0.1%
237000000 1
 
< 0.1%

homepage
Text

MISSING 

Distinct7673
Distinct (%)98.6%
Missing37684
Missing (%)82.9%
Memory size355.3 KiB
2024-04-25T17:21:05.971969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length242
Median length110
Mean length36.712799
Min length13

Characters and Unicode

Total characters285699
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7610 ?
Unique (%)97.8%

Sample

1st rowhttp://toystory.disney.com/toy-story
2nd rowhttp://www.mgm.com/view/movie/757/Goldeneye/
3rd rowhttp://www.mgm.com/title_title.do?title_star=LEAVINGL
4th rowhttp://www.sevenmovie.com/
5th rowhttp://www.mgm.com/#/our-titles/2083/The-Usual-Suspects
ValueCountFrequency (%)
http://www.georgecarlin.com 12
 
0.2%
iso_3166_1 7
 
0.1%
name 7
 
0.1%
http://www.wernerherzog.com/films-by.html 7
 
0.1%
http://breakblade.jp 6
 
0.1%
http://www.kungfupanda.com 6
 
0.1%
http://www.transformersmovie.com 5
 
0.1%
http://www.missionimpossible.com 5
 
0.1%
http://www.crownintlpictures.com/tztitles.html 4
 
0.1%
http://www.jeffdunham.com 4
 
0.1%
Other values (7658) 7753
99.2%
2024-04-25T17:21:06.170090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 25849
 
9.0%
/ 25820
 
9.0%
w 19516
 
6.8%
o 18783
 
6.6%
e 18709
 
6.5%
. 15387
 
5.4%
m 15101
 
5.3%
h 13863
 
4.9%
i 13654
 
4.8%
c 11414
 
4.0%
Other values (81) 107603
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 285699
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 25849
 
9.0%
/ 25820
 
9.0%
w 19516
 
6.8%
o 18783
 
6.6%
e 18709
 
6.5%
. 15387
 
5.4%
m 15101
 
5.3%
h 13863
 
4.9%
i 13654
 
4.8%
c 11414
 
4.0%
Other values (81) 107603
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 285699
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 25849
 
9.0%
/ 25820
 
9.0%
w 19516
 
6.8%
o 18783
 
6.6%
e 18709
 
6.5%
. 15387
 
5.4%
m 15101
 
5.3%
h 13863
 
4.9%
i 13654
 
4.8%
c 11414
 
4.0%
Other values (81) 107603
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 285699
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 25849
 
9.0%
/ 25820
 
9.0%
w 19516
 
6.8%
o 18783
 
6.6%
e 18709
 
6.5%
. 15387
 
5.4%
m 15101
 
5.3%
h 13863
 
4.9%
i 13654
 
4.8%
c 11414
 
4.0%
Other values (81) 107603
37.7%

id
Text

Distinct45436
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
2024-04-25T17:21:06.352409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.2514846
Min length1

Characters and Unicode

Total characters238764
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45407 ?
Unique (%)99.9%

Sample

1st row862
2nd row8844
3rd row15602
4th row31357
5th row11862
ValueCountFrequency (%)
141971 3
 
< 0.1%
12600 2
 
< 0.1%
109962 2
 
< 0.1%
69234 2
 
< 0.1%
5511 2
 
< 0.1%
159849 2
 
< 0.1%
25541 2
 
< 0.1%
42495 2
 
< 0.1%
298721 2
 
< 0.1%
14788 2
 
< 0.1%
Other values (45426) 45445
> 99.9%
2024-04-25T17:21:06.582800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%
Distinct45417
Distinct (%)99.9%
Missing17
Missing (%)< 0.1%
Memory size355.3 KiB
2024-04-25T17:21:06.768765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.9994719
Min length1

Characters and Unicode

Total characters409017
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45387 ?
Unique (%)99.9%

Sample

1st rowtt0114709
2nd rowtt0113497
3rd rowtt0113228
4th rowtt0114885
5th rowtt0113041
ValueCountFrequency (%)
tt1180333 3
 
< 0.1%
0 3
 
< 0.1%
tt0295682 2
 
< 0.1%
tt0100361 2
 
< 0.1%
tt1821641 2
 
< 0.1%
tt0062229 2
 
< 0.1%
tt0173769 2
 
< 0.1%
tt1327820 2
 
< 0.1%
tt0022879 2
 
< 0.1%
tt0111613 2
 
< 0.1%
Other values (45407) 45427
> 99.9%
2024-04-25T17:21:07.000883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 409017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 409017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 409017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%
Distinct92
Distinct (%)0.2%
Missing11
Missing (%)< 0.1%
Memory size355.3 KiB
2024-04-25T17:21:07.092371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.000154
Min length2

Characters and Unicode

Total characters90917
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en 32269
71.0%
fr 2438
 
5.4%
it 1529
 
3.4%
ja 1350
 
3.0%
de 1080
 
2.4%
es 994
 
2.2%
ru 826
 
1.8%
hi 508
 
1.1%
ko 444
 
1.0%
zh 409
 
0.9%
Other values (82) 3608
 
7.9%
2024-04-25T17:21:07.214126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%
Distinct43373
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
2024-04-25T17:21:07.390586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length109
Median length84
Mean length16.323494
Min length1

Characters and Unicode

Total characters742164
Distinct characters2946
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41712 ?
Unique (%)91.7%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II
ValueCountFrequency (%)
the 10261
 
7.8%
of 3309
 
2.5%
a 1674
 
1.3%
in 1275
 
1.0%
and 1072
 
0.8%
la 1007
 
0.8%
863
 
0.7%
to 806
 
0.6%
de 702
 
0.5%
man 509
 
0.4%
Other values (35324) 110301
83.7%
2024-04-25T17:21:07.719897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 742164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 742164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 742164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

overview
Text

MISSING 

Distinct44307
Distinct (%)99.5%
Missing954
Missing (%)2.1%
Memory size355.3 KiB
2024-04-25T17:21:07.916998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1000
Median length785
Mean length323.32155
Min length1

Characters and Unicode

Total characters14391689
Distinct characters429
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44247 ?
Unique (%)99.4%

Sample

1st rowLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.
2nd rowWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.
3rd rowA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.
4th rowCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.
5th rowJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.
ValueCountFrequency (%)
the 138357
 
5.6%
a 99037
 
4.0%
and 75407
 
3.1%
to 73442
 
3.0%
of 69723
 
2.8%
in 48228
 
2.0%
is 36550
 
1.5%
his 36210
 
1.5%
with 23933
 
1.0%
her 21518
 
0.9%
Other values (97181) 1830623
74.6%
2024-04-25T17:21:08.170843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2410599
16.7%
e 1366183
 
9.5%
a 942278
 
6.5%
t 936476
 
6.5%
i 853105
 
5.9%
o 831419
 
5.8%
n 824147
 
5.7%
s 769188
 
5.3%
r 745638
 
5.2%
h 601821
 
4.2%
Other values (419) 4110835
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14391689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2410599
16.7%
e 1366183
 
9.5%
a 942278
 
6.5%
t 936476
 
6.5%
i 853105
 
5.9%
o 831419
 
5.8%
n 824147
 
5.7%
s 769188
 
5.3%
r 745638
 
5.2%
h 601821
 
4.2%
Other values (419) 4110835
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14391689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2410599
16.7%
e 1366183
 
9.5%
a 942278
 
6.5%
t 936476
 
6.5%
i 853105
 
5.9%
o 831419
 
5.8%
n 824147
 
5.7%
s 769188
 
5.3%
r 745638
 
5.2%
h 601821
 
4.2%
Other values (419) 4110835
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14391689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2410599
16.7%
e 1366183
 
9.5%
a 942278
 
6.5%
t 936476
 
6.5%
i 853105
 
5.9%
o 831419
 
5.8%
n 824147
 
5.7%
s 769188
 
5.3%
r 745638
 
5.2%
h 601821
 
4.2%
Other values (419) 4110835
28.6%

popularity
Real number (ℝ)

SKEWED 

Distinct43757
Distinct (%)96.3%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.9214783
Minimum0
Maximum547.4883
Zeros66
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2024-04-25T17:21:08.244892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.018921
Q10.38594775
median1.127685
Q33.6789023
95-th percentile11.061568
Maximum547.4883
Range547.4883
Interquartile range (IQR)3.2929545

Descriptive statistics

Standard deviation6.0054143
Coefficient of variation (CV)2.055608
Kurtosis1925.684
Mean2.9214783
Median Absolute Deviation (MAD)0.9672565
Skewness29.225454
Sum132810.41
Variance36.065001
MonotonicityNot monotonic
2024-04-25T17:21:08.296470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66
 
0.1%
1 × 10-656
 
0.1%
0.000308 43
 
0.1%
0.00022 40
 
0.1%
0.000578 38
 
0.1%
0.001177 38
 
0.1%
0.000844 38
 
0.1%
0.002001 28
 
0.1%
0.003013 21
 
< 0.1%
0.00353 19
 
< 0.1%
Other values (43747) 45073
99.1%
ValueCountFrequency (%)
0 66
0.1%
1 × 10-656
0.1%
2 × 10-66
 
< 0.1%
3 × 10-66
 
< 0.1%
4 × 10-65
 
< 0.1%
5 × 10-61
 
< 0.1%
6 × 10-64
 
< 0.1%
7 × 10-61
 
< 0.1%
8 × 10-66
 
< 0.1%
9 × 10-62
 
< 0.1%
ValueCountFrequency (%)
547.488298 1
< 0.1%
294.337037 1
< 0.1%
287.253654 1
< 0.1%
228.032744 1
< 0.1%
213.849907 1
< 0.1%
187.860492 1
< 0.1%
185.330992 1
< 0.1%
185.070892 1
< 0.1%
183.870374 1
< 0.1%
154.801009 1
< 0.1%
Distinct17333
Distinct (%)38.2%
Missing90
Missing (%)0.2%
Memory size355.3 KiB
Minimum1874-12-09 00:00:00
Maximum2020-12-16 00:00:00
2024-04-25T17:21:08.350276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:08.408936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

revenue
Real number (ℝ)

ZEROS 

Distinct6863
Distinct (%)15.1%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11209349
Minimum0
Maximum2.7879651 × 109
Zeros38052
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2024-04-25T17:21:08.462647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile47808918
Maximum2.7879651 × 109
Range2.7879651 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64332247
Coefficient of variation (CV)5.7391602
Kurtosis237.51059
Mean11209349
Median Absolute Deviation (MAD)0
Skewness12.265983
Sum5.0957698 × 1011
Variance4.138638 × 1015
MonotonicityNot monotonic
2024-04-25T17:21:08.513931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38052
83.7%
12000000 20
 
< 0.1%
11000000 19
 
< 0.1%
10000000 19
 
< 0.1%
2000000 18
 
< 0.1%
6000000 17
 
< 0.1%
5000000 14
 
< 0.1%
8000000 13
 
< 0.1%
500000 13
 
< 0.1%
1 12
 
< 0.1%
Other values (6853) 7263
 
16.0%
ValueCountFrequency (%)
0 38052
83.7%
1 12
 
< 0.1%
2 3
 
< 0.1%
3 9
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2787965087 1
< 0.1%
2068223624 1
< 0.1%
1845034188 1
< 0.1%
1519557910 1
< 0.1%
1513528810 1
< 0.1%
1506249360 1
< 0.1%
1405403694 1
< 0.1%
1342000000 1
< 0.1%
1274219009 1
< 0.1%
1262886337 1
< 0.1%

runtime
Real number (ℝ)

ZEROS 

Distinct353
Distinct (%)0.8%
Missing263
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean94.128199
Minimum0
Maximum1256
Zeros1558
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2024-04-25T17:21:08.567546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q185
median95
Q3107
95-th percentile138
Maximum1256
Range1256
Interquartile range (IQR)22

Descriptive statistics

Standard deviation38.40781
Coefficient of variation (CV)0.40803724
Kurtosis93.217158
Mean94.128199
Median Absolute Deviation (MAD)11
Skewness4.4659579
Sum4254877
Variance1475.1599
MonotonicityNot monotonic
2024-04-25T17:21:08.621157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2556
 
5.6%
0 1558
 
3.4%
100 1470
 
3.2%
95 1412
 
3.1%
93 1214
 
2.7%
96 1104
 
2.4%
92 1080
 
2.4%
94 1062
 
2.3%
91 1057
 
2.3%
88 1032
 
2.3%
Other values (343) 31658
69.6%
ValueCountFrequency (%)
0 1558
3.4%
1 107
 
0.2%
2 33
 
0.1%
3 48
 
0.1%
4 51
 
0.1%
5 51
 
0.1%
6 72
 
0.2%
7 103
 
0.2%
8 78
 
0.2%
9 63
 
0.1%
ValueCountFrequency (%)
1256 1
< 0.1%
1140 2
< 0.1%
931 1
< 0.1%
925 1
< 0.1%
900 1
< 0.1%
877 1
< 0.1%
874 1
< 0.1%
840 2
< 0.1%
780 1
< 0.1%
720 1
< 0.1%

status
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing87
Missing (%)0.2%
Memory size355.3 KiB
Released
45014 
Rumored
 
230
Post Production
 
98
In Production
 
20
Planned
 
15

Length

Max length15
Median length8
Mean length8.0119218
Min length7

Characters and Unicode

Total characters363573
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReleased
2nd rowReleased
3rd rowReleased
4th rowReleased
5th rowReleased

Common Values

ValueCountFrequency (%)
Released 45014
99.0%
Rumored 230
 
0.5%
Post Production 98
 
0.2%
In Production 20
 
< 0.1%
Planned 15
 
< 0.1%
Canceled 2
 
< 0.1%
(Missing) 87
 
0.2%

Length

2024-04-25T17:21:08.675676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-25T17:21:08.720562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
released 45014
98.9%
rumored 230
 
0.5%
production 118
 
0.3%
post 98
 
0.2%
in 20
 
< 0.1%
planned 15
 
< 0.1%
canceled 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 363573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 363573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 363573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

tagline
Text

MISSING 

Distinct20283
Distinct (%)99.4%
Missing25054
Missing (%)55.1%
Memory size355.3 KiB
2024-04-25T17:21:08.876998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length297
Median length204
Mean length47.002841
Min length1

Characters and Unicode

Total characters959422
Distinct characters170
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20177 ?
Unique (%)98.8%

Sample

1st rowRoll the dice and unleash the excitement!
2nd rowStill Yelling. Still Fighting. Still Ready for Love.
3rd rowFriends are the people who let you be yourself... and never let you forget it.
4th rowJust When His World Is Back To Normal... He's In For The Surprise Of His Life!
5th rowA Los Angeles Crime Saga
ValueCountFrequency (%)
the 11004
 
6.3%
a 6820
 
3.9%
of 4406
 
2.5%
to 3586
 
2.1%
is 2800
 
1.6%
in 2693
 
1.5%
and 2686
 
1.5%
you 2389
 
1.4%
1585
 
0.9%
for 1524
 
0.9%
Other values (15108) 134566
77.3%
2024-04-25T17:21:09.126216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153795
16.0%
e 94486
 
9.8%
t 57309
 
6.0%
o 56611
 
5.9%
a 51521
 
5.4%
n 47539
 
5.0%
i 46086
 
4.8%
r 45029
 
4.7%
s 42399
 
4.4%
h 37192
 
3.9%
Other values (160) 327455
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 959422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
153795
16.0%
e 94486
 
9.8%
t 57309
 
6.0%
o 56611
 
5.9%
a 51521
 
5.4%
n 47539
 
5.0%
i 46086
 
4.8%
r 45029
 
4.7%
s 42399
 
4.4%
h 37192
 
3.9%
Other values (160) 327455
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 959422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
153795
16.0%
e 94486
 
9.8%
t 57309
 
6.0%
o 56611
 
5.9%
a 51521
 
5.4%
n 47539
 
5.0%
i 46086
 
4.8%
r 45029
 
4.7%
s 42399
 
4.4%
h 37192
 
3.9%
Other values (160) 327455
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 959422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
153795
16.0%
e 94486
 
9.8%
t 57309
 
6.0%
o 56611
 
5.9%
a 51521
 
5.4%
n 47539
 
5.0%
i 46086
 
4.8%
r 45029
 
4.7%
s 42399
 
4.4%
h 37192
 
3.9%
Other values (160) 327455
34.1%

title
Text

Distinct42277
Distinct (%)93.0%
Missing6
Missing (%)< 0.1%
Memory size355.3 KiB
2024-04-25T17:21:09.315366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length105
Median length79
Mean length16.708535
Min length1

Characters and Unicode

Total characters759570
Distinct characters287
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39947 ?
Unique (%)87.9%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II
ValueCountFrequency (%)
the 14571
 
10.7%
of 4938
 
3.6%
a 2244
 
1.6%
in 1697
 
1.2%
and 1634
 
1.2%
to 1055
 
0.8%
763
 
0.6%
man 665
 
0.5%
love 664
 
0.5%
for 602
 
0.4%
Other values (24431) 107634
78.9%
2024-04-25T17:21:09.563880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 759570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 759570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 759570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

video
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.5 KiB
False
45373 
True
 
93
ValueCountFrequency (%)
False 45373
99.8%
True 93
 
0.2%
2024-04-25T17:21:09.630873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

vote_average
Real number (ℝ)

ZEROS 

Distinct92
Distinct (%)0.2%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.6182072
Minimum0
Maximum10
Zeros2998
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2024-04-25T17:21:09.743503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q36.8
95-th percentile7.8
Maximum10
Range10
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.924216
Coefficient of variation (CV)0.34249644
Kurtosis2.5004022
Mean5.6182072
Median Absolute Deviation (MAD)0.9
Skewness-1.5189901
Sum255403.7
Variance3.7026072
MonotonicityNot monotonic
2024-04-25T17:21:09.794962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2998
 
6.6%
6 2468
 
5.4%
5 2001
 
4.4%
7 1886
 
4.1%
6.5 1722
 
3.8%
6.3 1603
 
3.5%
5.5 1381
 
3.0%
5.8 1369
 
3.0%
6.4 1350
 
3.0%
6.7 1342
 
3.0%
Other values (82) 27340
60.1%
ValueCountFrequency (%)
0 2998
6.6%
0.5 13
 
< 0.1%
0.7 1
 
< 0.1%
1 105
 
0.2%
1.1 1
 
< 0.1%
1.2 4
 
< 0.1%
1.3 13
 
< 0.1%
1.4 5
 
< 0.1%
1.5 30
 
0.1%
1.6 6
 
< 0.1%
ValueCountFrequency (%)
10 190
0.4%
9.8 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 18
 
< 0.1%
9.4 3
 
< 0.1%
9.3 18
 
< 0.1%
9.2 4
 
< 0.1%
9.1 3
 
< 0.1%
9 159
0.3%
8.9 7
 
< 0.1%

vote_count
Real number (ℝ)

ZEROS 

Distinct1820
Distinct (%)4.0%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean109.89734
Minimum0
Maximum14075
Zeros2899
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2024-04-25T17:21:09.842484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q334
95-th percentile434
Maximum14075
Range14075
Interquartile range (IQR)31

Descriptive statistics

Standard deviation491.31037
Coefficient of variation (CV)4.4706303
Kurtosis151.2028
Mean109.89734
Median Absolute Deviation (MAD)8
Skewness10.450232
Sum4995933
Variance241385.88
MonotonicityNot monotonic
2024-04-25T17:21:09.892067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3264
 
7.2%
2 3132
 
6.9%
0 2899
 
6.4%
3 2787
 
6.1%
4 2480
 
5.5%
5 2097
 
4.6%
6 1747
 
3.8%
7 1570
 
3.5%
8 1359
 
3.0%
9 1194
 
2.6%
Other values (1810) 22931
50.4%
ValueCountFrequency (%)
0 2899
6.4%
1 3264
7.2%
2 3132
6.9%
3 2787
6.1%
4 2480
5.5%
5 2097
4.6%
6 1747
3.8%
7 1570
3.5%
8 1359
3.0%
9 1194
 
2.6%
ValueCountFrequency (%)
14075 1
< 0.1%
12269 1
< 0.1%
12114 1
< 0.1%
12000 1
< 0.1%
11444 1
< 0.1%
11187 1
< 0.1%
10297 1
< 0.1%
10014 1
< 0.1%
9678 1
< 0.1%
9634 1
< 0.1%

Interactions

2024-04-25T17:21:05.043656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:03.705915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.011648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.251369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.485353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.810753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:05.086319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:03.791651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.052872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.292197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.530855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.851718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:05.126991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:03.836388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.092843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.328798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.573054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.888940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:05.166846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:03.879611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.131441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.366935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.615465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.926910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:05.208930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:03.925672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.171110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.405416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.727794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.965686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:05.250281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:03.966790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.210620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.444096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:04.769267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-25T17:21:05.002435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-25T17:21:05.340220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-25T17:21:05.477668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

adultbudgethomepageidimdb_idoriginal_languageoriginal_titleoverviewpopularityrelease_daterevenueruntimestatustaglinetitlevideovote_averagevote_count
0False30000000.0http://toystory.disney.com/toy-story862tt0114709enToy StoryLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.21.9469431995-10-30373554033.081.0ReleasedNaNToy StoryFalse7.75415.0
1False65000000.0NaN8844tt0113497enJumanjiWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.17.0155391995-12-15262797249.0104.0ReleasedRoll the dice and unleash the excitement!JumanjiFalse6.92413.0
2False0.0NaN15602tt0113228enGrumpier Old MenA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.11.7129001995-12-220.0101.0ReleasedStill Yelling. Still Fighting. Still Ready for Love.Grumpier Old MenFalse6.592.0
3False16000000.0NaN31357tt0114885enWaiting to ExhaleCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.3.8594951995-12-2281452156.0127.0ReleasedFriends are the people who let you be yourself... and never let you forget it.Waiting to ExhaleFalse6.134.0
4False0.0NaN11862tt0113041enFather of the Bride Part IIJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.8.3875191995-02-1076578911.0106.0ReleasedJust When His World Is Back To Normal... He's In For The Surprise Of His Life!Father of the Bride Part IIFalse5.7173.0
5False60000000.0NaN949tt0113277enHeatObsessive master thief, Neil McCauley leads a top-notch crew on various insane heists throughout Los Angeles while a mentally unstable detective, Vincent Hanna pursues him without rest. Each man recognizes and respects the ability and the dedication of the other even though they are aware their cat-and-mouse game may end in violence.17.9249271995-12-15187436818.0170.0ReleasedA Los Angeles Crime SagaHeatFalse7.71886.0
6False58000000.0NaN11860tt0114319enSabrinaAn ugly duckling having undergone a remarkable change, still harbors feelings for her crush: a carefree playboy, but not before his business-focused brother has something to say about it.6.6772771995-12-150.0127.0ReleasedYou are cordially invited to the most surprising merger of the year.SabrinaFalse6.2141.0
7False0.0NaN45325tt0112302enTom and HuckA mischievous young boy, Tom Sawyer, witnesses a murder by the deadly Injun Joe. Tom becomes friends with Huckleberry Finn, a boy with no future and no family. Tom has to choose between honoring a friendship or honoring an oath because the town alcoholic is accused of the murder. Tom and Huck go through several adventures trying to retrieve evidence.2.5611611995-12-220.097.0ReleasedThe Original Bad Boys.Tom and HuckFalse5.445.0
8False0.0NaN47686tt0119019enDream with the FishesTerry is a suicidal voyeur who treats a dying addict to a final binge, but Terry will only do this if he promises to kill him.0.6841921997-01-010.097.0ReleasedAn oddball odyssey about voyeurism, LSD and nude bowling!Dream with the FishesFalse7.710.0
9False35000000.0NaN9091tt0114576enSudden DeathInternational action superstar Jean Claude Van Damme teams with Powers Boothe in a Tension-packed, suspense thriller, set against the back-drop of a Stanley Cup game.Van Damme portrays a father whose daughter is suddenly taken during a championship hockey game. With the captors demanding a billion dollars by game's end, Van Damme frantically sets a plan in motion to rescue his daughter and abort an impending explosion before the final buzzer...5.2315801995-12-2264350171.0106.0ReleasedTerror goes into overtime.Sudden DeathFalse5.5174.0
adultbudgethomepageidimdb_idoriginal_languageoriginal_titleoverviewpopularityrelease_daterevenueruntimestatustaglinetitlevideovote_averagevote_count
45456False0.0NaN84419tt0038621enHouse of HorrorsAn unsuccessful sculptor saves a madman named "The Creeper" from drowning. Seeing an opportunity for revenge, he tricks the psycho into murdering his critics.0.2228141946-03-290.065.0ReleasedMeet...The CREEPER!House of HorrorsFalse6.38.0
45457False0.0NaN390959tt0265736enShadow of the Blair WitchIn this true-crime documentary, we delve into the murder spree that was the inspiration for Joe Berlinger's "Book of Shadows: Blair Witch 2".0.0760612000-10-220.045.0ReleasedNaNShadow of the Blair WitchFalse7.02.0
45458False0.0NaN289923tt0252966enThe Burkittsville 7A film archivist revisits the story of Rustin Parr, a hermit thought to have murdered seven children while under the possession of the Blair Witch.0.3864502000-10-030.030.0ReleasedDo you know what happened 50 years before "The Blair Witch Project"?The Burkittsville 7False7.01.0
45459False0.0NaN222848tt0112613enCaged Heat 3000It's the year 3000 AD. The world's most dangerous women are banished to a remote asteroid 45 million light years from earth. Kira Murphy doesn't belong; wrongfully accused of a crime she did not commit, she's thrown in this interplanetary prison and left to her own defenses. But Kira's a fighter, and soon she finds herself in the middle of a female gang war; where everyone wants a piece of the action... and a piece of her! "Caged Heat 3000" takes the Women-in-Prison genre to a whole new level... and a whole new galaxy!0.6615581995-01-010.085.0ReleasedNaNCaged Heat 3000False3.51.0
45460False0.0NaN30840tt0102797enRobin HoodYet another version of the classic epic, with enough variation to make it interesting. The story is the same, but some of the characters are quite different from the usual, in particular Uma Thurman's very special maid Marian. The photography is also great, giving the story a somewhat darker tone.5.6837531991-05-130.0104.0ReleasedNaNRobin HoodFalse5.726.0
45461False0.0http://www.imdb.com/title/tt6209470/439050tt6209470faرگ خوابRising and falling between a man and woman.0.072051NaT0.090.0ReleasedRising and falling between a man and womanSubdueFalse4.01.0
45462False0.0NaN111109tt2028550tlSiglo ng PagluluwalAn artist struggles to finish his work while a storyline about a cult plays in his head.0.1782412011-11-170.0360.0ReleasedNaNCentury of BirthingFalse9.03.0
45463False0.0NaN67758tt0303758enBetrayalWhen one of her hits goes wrong, a professional assassin ends up with a suitcase full of a million dollars belonging to a mob boss ...0.9030072003-08-010.090.0ReleasedA deadly game of wits.BetrayalFalse3.86.0
45464False0.0NaN227506tt0008536enSatana likuyushchiyIn a small town live two brothers, one a minister and the other one a hunchback painter of the chapel who lives with his wife. One dreadful and stormy night, a stranger knocks at the door asking for shelter. The stranger talks about all the good things of the earthly life the minister is missing because of his puritanical faith. The minister comes to accept the stranger's viewpoint but it is others who will pay the consequences because the minister will discover the human pleasures thanks to, ehem, his sister- in -law… The tormented minister and his cuckolded brother will die in a strange accident in the chapel and later an infant will be born from the minister's adulterous relationship.0.0035031917-10-210.087.0ReleasedNaNSatan TriumphantFalse0.00.0
45465False0.0NaN461257tt6980792enQueerama50 years after decriminalisation of homosexuality in the UK, director Daisy Asquith mines the jewels of the BFI archive to take us into the relationships, desires, fears and expressions of gay men and women in the 20th century.0.1630152017-06-090.075.0ReleasedNaNQueeramaFalse0.00.0

Duplicate rows

Most frequently occurring

adultbudgethomepageidimdb_idoriginal_languageoriginal_titleoverviewpopularityrelease_daterevenueruntimestatustaglinetitlevideovote_averagevote_count# duplicates
4False0.0NaN141971tt1180333fiBlackoutRecovering from a nail gun shot to the head and 13 months of coma, doctor Pekka Valinta starts to unravel the mystery of his past, still suffering from total amnesia.0.4119492008-12-260.0108.0ReleasedWhich one is the first to return - memory or the murderer?BlackoutFalse6.73.03
0False0.0http://www.daysofdarknessthemovie.com/18440tt0499456enDays of DarknessWhen a comet strikes Earth and kicks up a cloud of toxic dust, hundreds of humans join the ranks of the living dead. But there's bad news for the survivors: The newly minted zombies are hell-bent on eradicating every last person from the planet. For the few human beings who remain, going head to head with the flesh-eating fiends is their only chance for long-term survival. Yet their battle will be dark and cold, with overwhelming odds.1.4360852007-01-010.089.0ReleasedNaNDays of DarknessFalse5.05.02
1False0.0http://www.dealthemovie.com/11115tt0446676enDealAs an ex-gambler teaches a hot-shot college kid some things about playing cards, he finds himself pulled into the world series of poker, where his protégé is his toughest competition.6.8803652008-01-290.085.0ReleasedNaNDealFalse5.222.02
2False0.0NaN105045tt0111613deDas VersprechenEast-Berlin, 1961, shortly after the erection of the Wall. Konrad, Sophie and three of their friends plan a daring escape to Western Germany. The attempt is successful, except for Konrad, who remains behind. From then on, and for the next 28 years, Konrad and Sophie will attempt to meet again, in spite of the Iron Curtain. Konrad, who has become a reputed Astrophysicist, tries to take advantage of scientific congresses outside Eastern Germany to arrange encounters with Sophie. But in a country where the political police, the Stasi, monitors the moves of all suspicious people (such as Konrad's sister Barbara and her husband Harald), preserving one's privacy, ideals and self-respect becomes an exhausting fight, even as the Eastern block begins its long process of disintegration.0.1221781995-02-160.0115.0ReleasedA love, a hope, a wall.The PromiseFalse5.01.02
3False0.0NaN119916tt0080000enThe TempestProspero, the true Duke of Milan is now living on an enchanted island with his daughter Miranda, the savage Caliban and Ariel, a spirit of the air. Raising a sorm to bring his brother - the usurper of his dukedom - along with his royal entourage. to the island. Prospero contrives his revenge.0.0000181980-02-270.0123.0ReleasedNaNThe TempestFalse0.00.02
5False0.0NaN152795tt1821641enThe CongressMore than two decades after catapulting to stardom with The Princess Bride, an aging actress (Robin Wright, playing a version of herself) decides to take her final job: preserving her digital likeness for a future Hollywood. Through a deal brokered by her loyal, longtime agent and the head of Miramount Studios, her alias will be controlled by the studio, and will star in any film they want with no restrictions. In return, she receives healthy compensation so she can care for her ailing son and her digitized character will stay forever young. Twenty years later, under the creative vision of the studio’s head animator, Wright’s digital double rises to immortal stardom. With her contract expiring, she is invited to take part in “The Congress” convention as she makes her comeback straight into the world of future fantasy cinema.8.5340392013-05-16455815.0122.0ReleasedNaNThe CongressFalse6.4165.02
6False0.0NaN159849tt0173769enWhy We Fight: Divide and ConquerThe third film of Frank Capra's 'Why We Fight" propaganda film series, dealing with the Nazi conquest of Western Europe in 1940.0.4733221943-01-010.057.0ReleasedNaNWhy We Fight: Divide and ConquerFalse5.01.02
7False0.0NaN168538tt0084387enNanaIn Zola's Paris, an ingenue arrives at a tony bordello: she's Nana, guileless, but quickly learning to use her erotic innocence to get what she wants. She's an actress for a soft-core filmmaker and soon is the most popular courtesan in Paris, parlaying this into a house, bought for her by a wealthy banker. She tosses him and takes up with her neighbor, a count of impeccable rectitude, and with the count's impressionable son. The count is soon fetching sticks like a dog and mortgaging his lands to satisfy her whims.1.2766021983-06-130.092.0ReleasedNaNNana, the True Key of PleasureFalse4.73.02
8False0.0NaN23305tt0295682enThe WarriorIn feudal India, a warrior (Khan) who renounces his role as the longtime enforcer to a local lord becomes the prey in a murderous hunt through the Himalayan mountains.1.9679922001-09-230.086.0ReleasedNaNThe WarriorFalse6.315.02
9False0.0NaN25541tt1327820daBroderskabFormer Danish servicemen Lars and Jimmy are thrown together while training in a neo-Nazi group. Moving from hostility through grudging admiration to friendship and finally passion, events take a darker turn when their illicit relationship is uncovered.2.5879112009-10-210.090.0ReleasedNaNBrotherhoodFalse7.121.02